AIMC Topic: Terminal Care

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Artificial Intelligence and Aging in Place: A Scoping Review of Current Applications and Future Directions.

The Gerontologist
BACKGROUND AND OBJECTIVES: As the global population continues to age, aging in place (AIP) has emerged as an essential strategy to help older adults live safely and independently within their communities. Although artificial intelligence (AI) holds p...

Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6-month mortality and improve end-of-life outcomes for advanced cancer patients.

Cancer medicine
BACKGROUND: There are significant disparities in outcomes at the end-of-life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions...

Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.

JCO clinical cancer informatics
PURPOSE: Data on end-of-life care (EOLC) quality, assessed through evidence-based quality measures (QMs), are difficult to obtain. Natural language processing (NLP) enables efficient quality measurement and is not yet used for children with serious i...

When Will Death Be? Legal Considerations and Regulatory Safeguards in Predictive Modelling Applications for End-of-Life Care.

Journal of law and medicine
Advance care planning (ACP) is generally considered as valuable in guiding treatments that are aligned with patients' preferences. Despite its benefits, there are some practical and legal difficulties in its implementation. Predictive modelling is in...

Measuring Processes of Care in Palliative Surgery: A Novel Approach Using Natural Language Processing.

Annals of surgery
: Palliative surgical procedures are often performed for patients with limited survival. Quality measures for processes of care at the end of life are appropriate in palliative surgery, but have not been applied in this patient population. In this pa...